An Enhanced Immunoaffinity Enrichment Method for Mass Spectrometry–Based Translational Proteomics

Mar 01, 2014
Volume 12, Issue 1

Translational proteomics has not been as successful as originally anticipated. Because mass spectrometry (MS) can separate proteins at the sequence level, it provides the selectivity needed for this application; however, traditional challenges still exist, including time-to-result, throughput, and sample-size requirements. For analytical validation and verification purposes, sample preparation times must be reduced from days to hours. Scientists recently coupled a previously developed immunoaffinity enrichment method to selected reaction monitoring (SRM) MS. The MS immunoassay–SRM method combines liquid chromatography–tandem mass spectrometry (LC–MS-MS) for target identification, a microscale immunoaffinity capture method for enrichment, and subsequent SRM analysis. Using the MS immunoassay–SRM workflow, a standard high-throughput method for developing targeted biomarker identification of proteins in human plasma and serum for clinical research was developed.

Mass spectrometry (MS)-driven proteomics has made progress in the identification and quantification of disease biomarkers, including C-reactive protein as an indicator for myocardial infarction and prostate-specific antigen (PSA) for prostate cancer. Despite these and other successes, translational proteomics, which is defined as the translation of biomarker discovery to routine analysis, has not been nearly as successful as originally anticipated because comprehensive proteomic analysis of plasma, serum, and other biological fluids has proved exceedingly challenging. The "look alike" nature of molecular isoforms, the enormous dynamic range of protein concentrations of potential interest (>10 orders of magnitude in blood plasma), and the fact that molecules of interest are often in low abundance have all slowed the progress of biomarker hunters across the globe.

One example of the challenge presented by protein analyte isoforms is demonstrated by the need to distinguish between full-length parathyroid hormone (PTH) 1-84 and multiple N-terminally truncated PTH variants. The differences between these isoforms are critical to accurate diagnosis of endocrine and osteological diseases (1). Similarly, in clinical testing, PSA typically presents in truncated and modified isoforms, making precise detection and quantification difficult which contributes to a high false-positive rate (2).

From a processing point of view, binding protein detachment and the high dynamic range of samples have hampered assays for insulin-like growth factor 1 (IGF-1), a marker for growth-related illness that is important in cell proliferation, differentiation, apoptosis, and tissue growth from a research point of view (3).

To add to these challenges, verification and population-scale biomarker validation require the analysis of hundreds or even thousands of high-quality samples. Sample collection and storage must use standard protocols to reduce potential variations attributable to endogenous enzymes or sample contamination. Verification and validation studies require multiple control groups and subjects in disease subcategories — all gathered over the course of disease progression. The analysis of many samples is required to distinguish normal human genetic heterogeneity and heterogeneity attributable to disease. High-throughput detection methods are essential to achieving statistical confidence in the accurate identification of molecules of interest (4).

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